mlsanityData to 7 Oct 2026

Auto-Encoding Molecular Conformations

arXiv 2101.01618 ↗First posted 5 Jan 2021cs.LG physics.chem-ph q-bio.QMcited by 8

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Abstract (arXiv)

In this work we introduce an Autoencoder for molecular conformations. Our proposed model converts the discrete spatial arrangements of atoms in a given molecular graph (conformation) into and from a continuous fixed-sized latent representation. We demonstrate that in this latent representation, similar conformations cluster together while distinct conformations split apart. Moreover, by training a probabilistic model on a large dataset of molecular conformations, we demonstrate how our model can be used to generate diverse sets of energetically favorable conformations for a given molecule. Finally, we show that the continuous representation allows us to utilize optimization methods to find molecules that have conformations with favourable spatial properties.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 10 of its 24 references that are in the map. First posted on arXiv 5 Jan 2021.

Builds on

10 of 24 references.

References · 10

  1. 2017Graph Attention Networkscited by 8,854
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2017Deep Setscited by 1,394
  4. 2016Automatic chemical design using a data-driven continuous representation of moleculescited by 762
  5. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  6. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  7. 2017Learning to Plan Chemical Synthesescited by 175
  8. 2019Molecular geometry prediction using a deep generative graph neural networkcited by 91
  9. 2019A Generative Model for Molecular Distance Geometrycited by 73
  10. 2019Generating valid Euclidean distance matricescited by 19

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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 1; 2025: 1.

8 papers.

  1. 2023Geometric Latent Diffusion Models for 3D Molecule Generationcited by 185
  2. 2022Direct Molecular Conformation Generationcited by 30
  3. 2021Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotationscited by 29
  4. 2023Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamicscited by 14
  5. 2023Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoderscited by 3
  6. 2024Multi-Type Point Cloud Autoencoder: A Complete Equivariant Embedding for Molecule Conformation and Posecited by 3
  7. 2025Manipulating 3D Molecules in a Fixed-Dimensional E(3)-Equivariant Latent Spacecited by 3
  8. 2021Equivalent Distance Geometry Error for Molecular Conformation Comparisoncited by 0